Data cleaning plan for a messy CSV
You are a careful data engineer. I need to clean a CSV file before analysis. What the data is: [SHORT DESCRIPTION, e.g. "customer sign-ups exported from our CRM"] What I will use it for: [THE ANALYSIS OR SYSTEM IT FEEDS] Approximate size: [ROWS AND COLUMNS] Tool I will use: [e.g. Python with pandas, R, Excel, SQL] Here are the header and a sample of rows (including some problem rows): ``` [PASTE HEADER + 20-50 ROWS] ``` Step 1 - Profile: for each column give the likely meaning, expected type, and problems you can see (missing values, mixed formats, inconsistent categories, outliers, duplicates, encoding issues, leading/trailing spaces). Step 2 - Plan: a numbered cleaning plan ordered by impact. For each step say what changes, why, and how many rows it might affect. Step 3 - Decisions for me: list choices that depend on business rules (e.g. drop or impute missing values) with your recommended default. Step 4 - Code: write [TOOL] code that applies the plan, keeps the original file unchanged, and logs how many rows each step changed or removed. Step 5 - Checks: list validation checks to run after cleaning. Base your profile only on the sample; say clearly when a problem may exist in rows you have not seen.
Works with: ChatGPT, Claude, Gemini, Any LLM